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Sports Statistics EasierFri, 31 Jul 2015 21:24:02 +0000en-UShourly1http://wordpress.org/?v=4.2.3Kevin Garnett vs the Trade Voltronhttp://www.sports-reference.com/blog/2015/07/kevin-garnett-vs-the-trade-voltron/
http://www.sports-reference.com/blog/2015/07/kevin-garnett-vs-the-trade-voltron/#commentsFri, 31 Jul 2015 21:24:02 +0000http://www.sports-reference.com/blog/?p=4328Today is the 8th anniversary of the Kevin Garnett trade. Of course, there have now been 3 Kevin Garnett trades, but you know the one I'm talking about. In 2007, the Minnesota Timberwolves changed NBA history by sending KG to Boston in exchange for Al Jefferson, Ryan Gomes, Gerald Green, Sebastian Telfair, Theo Ratliff, and the picks that would be come Wayne Ellington and Jonny Flynn.

As you know, that trade went much better for Boston and KG than it did for the Timberwolves, who haven't been to the playoffs since. It's easy to criticize this trade, but Minnesota was in a difficult position for two reasons. First, Kevin Garnett is a once-in-a-generation talent, so almost no trade could have brought back a fair return. And, second, they couldn't build a Voltron out of the players they received in the trade.

While there's nothing we can do about the former, we decided to celebrate the anniversary of the trade by doing the latter. I give you, the Kevin Garnett Trade Voltron:

First of all, thank you for not immediately closing the tab in horror. Second, you may be wondering how we built the Voltron. What we did was combine every minute, rebound, assist, steal, block, and point put up by a player in the KG trade during their time in Minnesota (we didn't count Telfair's 2010 stint there, since he was traded away in 2009 and then returned in a second trade later). If you add it all together, here's what you get:

On sheer volume, KG can't compete with Voltron, who played over twice as many minutes and thus has higher counting stats. But we also keep track of Per 36 Minute numbers. To calculate per 36 minute numbers, you divide a player's raw stat, say total points, by the number of minutes they played to get a per minute number. Then you multiple that number by 36. Why do that? Well when you're comparing players with vastly different minutes totals, it lets you see them on an even playing field. By using 36 minutes as a baseline, we get totals that look similar to the kinds of per game stats we're used to seeing.

So, with that in mind, can KG defeat Voltron on Per 36 Minute numbers?

Poor Voltron. Despite containing two point guards, Voltron can't even beat KG on assists or steals per 36 minutes. Despite containing a center reknowned for his scoring prowess, Voltron is still 3 rebounds and 3 points per 36 minutes below KG. And, well, the blocks were never going to be fair.

However, I wanted to understand my creation, so I did a Season Finder search to see what, exactly, I had built. While there's no perfect match, it seemed like the closest player to my creation was a less block-heavy 1999-00 T-Mac.

Either way, there you have it. Definite, statistical proof that KG tougher than Voltron.

]]>http://www.sports-reference.com/blog/2015/07/kevin-garnett-vs-the-trade-voltron/feed/0Get Ready for the 2015 NBA Drafthttp://www.sports-reference.com/blog/2015/06/get-ready-for-the-2015-nba-draft/
http://www.sports-reference.com/blog/2015/06/get-ready-for-the-2015-nba-draft/#commentsTue, 23 Jun 2015 18:00:32 +0000http://www.sports-reference.com/blog/?p=4290The 2015 NBA Draft will take place Thursday night, June 25. Here's some draft info you should check out before, during & after the draft:

2015 NBA Draft Preview: See the top picks of all-time from every slot, as well as Expected Value of each pick.

]]>http://www.sports-reference.com/blog/2015/06/get-ready-for-the-2015-nba-draft/feed/0NBA Draft-related Additions to Basketball Reference Play Indexhttp://www.sports-reference.com/blog/2015/06/nba-draft-related-additions-to-basketball-reference-play-index/
http://www.sports-reference.com/blog/2015/06/nba-draft-related-additions-to-basketball-reference-play-index/#commentsThu, 18 Jun 2015 16:48:28 +0000http://www.sports-reference.com/blog/?p=4280With the NBA Draft a week away, we wanted to briefly introduce some subtle, but useful additions to the Play Index.

First up, is a tweak to the Player Season Finder, which now allows for customized statistical searches by player draft year. You'll notice there's now a filter for BAA/NBA Draft Year. Utilizing this, you can now search for things such as:

Additionally, we have made some enhancements to the Draft Finder. The biggest change is that you can now filter by the draftees' NBA/BAA position (please note that we don't have positions in the DB for many drafted players who never went on to play in the league). But, for instance, here are centers drafted in 2014. Additionally, you'll notice that player ages on draft day and birth country now display in the results. Again, this data is unavailable for many players who never played in the league.

We hope you find these new tools useful.

]]>http://www.sports-reference.com/blog/2015/06/nba-draft-related-additions-to-basketball-reference-play-index/feed/1Find NBA Finals Data on Basketball-Referencehttp://www.sports-reference.com/blog/2015/06/find-nba-finals-data-on-basketball-reference/
http://www.sports-reference.com/blog/2015/06/find-nba-finals-data-on-basketball-reference/#commentsThu, 04 Jun 2015 19:22:49 +0000http://www.sports-reference.com/blog/?p=4256With the NBA Finals (finally) tipping off tonight, we just wanted to quickly link to some of the NBA Finals data & tools we have on the site.

For a full list of NBA Champions, Finals MVPs & annual top playoff performers click here.

For a full list of every Playoff series in NBA history click here. You can also click on any series on that list to see a cumulative box score for that series, with links to every game. Here is the 2014 NBA Finals, for instance.

Additionally, you can use our Play Index to run searches specifically for NBA Finals games. In the Team Game Finder or Player Game Finder, just set the game type filter to Playoffs and the round filter to Finals like in the image:

We've been working recently to make our European stats better and easier to access. Toward that end, I wanted to introduce a few recently-added features that make it easier to find detailed European league player and team stats, standings, and game logs. In case you haven't spent time on our European league sites, know that we maintain sites for the Euroleague and Eurocup competitions as well as Liga ACB (Spain), LNB Pro A (France), Lega Serie A (Italy), and the Greek Basket League.

Foremost, our European sites share a new, easy-to-find homepage at http://www.basketball-reference.com/euro. The homepage has six large links that serve as a portal to each league's individual home. Each league's page looks much like our NBA homepage, with a rotating leaderboard, league standings, results from recent games, top performers, and more.

We've also added player game logs and box scores to the site, for example this box from Real Madrid's Euroleague clinching game. To find the gamelogs, visit any active player's page and mouse over the 'Gamelogs' box, and you'll see a link for game logs from each league in which the player has appeared (see below). Links to box scores are available on the respective league homepages, team schedule pages, and are also linked from the player game logs.

We've also added a new European stats section to the bottom-right of our homepage, with links to each league and the most recent day's games:

We occasionally get emails from users who have interesting research to share. Recently, Todd Spehr (@toddspehr35) forwarded box scores from the NBA All-Star Legends Games that were played for a decade in the mid-80s until the 90s. It would seem out of this world to modern fans, but the NBA staged an annual exhibition of living legends during its All-Star festivities. Hall of Famers John Havlicek, Walt Bellamy, and Artis Gilmore were among the participants until the exhibition ran its course in 1993.

You can find links to these box scores from any page of our comprehensive all-star archives. In particular, look for the Legends Game tab and hover your mouse for a link to every box from the 1980s and 1990s.

I'm excited to announce that we've extended our historical College Basketball schedules back to 1949-50. Previously, we had published them back to 1979-80, and had displayed a scanned image of the team's schedule when available. These three decades of additions supplements our full collection of historical NCAA Tournament box scores.

Just wanted to show off a couple new features that we've recently introduced...

When you find yourself on our team season pages look for the "Game-by-game roster status" links:

These will bring you to a Roster Status page with a color-coded visualization of each player's roster status for the 82 regular season games, and the playoffs. Designations include Starter, Reserve, DNP, Inactive, and Suspended. Note that a player's status is also noted on boxscores and our player gamelogs. Notable in 2014-15 are the Raptors, Bulls, and Magic, all of whom maintained a stable roster throughout the season. The Sixers page is a lot of fun to look at, too.

We've published these going back to 2013-14, and should hopefully extend that period at some point.

The other new feature is a historical time series of roster continuity for each currently-active franchise. Our accounting begins with 1952-53 given that the season prior is the first for which we have Minutes Played data for every league player.

Several years ago former colleague Neil Paine publishedsomemusings on our blog about Team Continuity, and Dean Oliver devotes a couple pages in his Basketball on Paper book in the context of historically bad teams. The Spurs are well-noted stalwarts of year-to-year stability - on the other hand, the Cavaliers and Mavericks show that it's possible to turn over most of your minutes and not come apart at the seams, especially if one of your newcomers is LeBron James.

]]>http://www.sports-reference.com/blog/2015/05/new-team-roster-features/feed/0The Impact of Multi-Player Trades on Performance in the NBAhttp://www.sports-reference.com/blog/2015/04/the-impact-of-multi-player-trades-on-performance-in-the-nba/
http://www.sports-reference.com/blog/2015/04/the-impact-of-multi-player-trades-on-performance-in-the-nba/#commentsMon, 27 Apr 2015 17:05:38 +0000http://www.sports-reference.com/blog/?p=4158Sometimes SR data finds its way into academic journals. Here's a summary Benjamin Campbell has written up on some of his findings about post-trade player performance:

"Although NBA GMs make mid-season trades for multiple reasons, one frequent objective is to improve

the short-term performance of the team. Since the rim is 10 feet from the floor everywhere from Hinkle

Fieldhouse to the Staples Center and the rules are the same everywhere, this seems to be a good

strategy. However, given the interdependent nature of basketball, trades present a challenge to short-

term performance because they disrupt the ability of players to productively play together. It is through

experience and time together that players can learn how to best play together, thus there is a learning

curve whenever a trade occurs. This learning curve impacts both the players joining a new team and the

incumbent players on that team that now have to learn to play with new players.

The learning curve for players to adjust to a trade is impacted by the size of a trade. For example, when

Raef LaFrentz, Nick van Exel, Avery Johnson, and Tariq Abdul-Wahad moved together from Denver to

Dallas in exchange for three players on February 21, 2002, they had less of a learning curve than a single

mover because they already knew how best to play with each other. However, their new teammates in

Dallas had a steeper learning curve because the incumbent players have to learn the idiosyncrasies of

four new, already coordinated teammates.

Using data from basketball-reference.com, a recent academic paper explores the learning curve

associated with single and multi-player trades on player performance over time. The authors find that

players who move from one team to another by themselves lose 2.3 percentage points from their true

shooting percentage on average, and take about 20 games to get back to their previous performance.

The true shooting percentage of players who move as part of a multi-player trade is not significantly

impacted. However, the reverse is true for incumbent players: players who are joined by one new

teammate experience no reduction in team shooting percentage, but players who are joined by multiple

teammates at the same time do experience a small (but statistically insignificant) reduction in true

shooting percentage. These effects are similar for both starters and little-used players alike.

The authors also show that moving with other players has a substantially larger positive effect on

movers’ individual performance when moving to teams with a losing record than when moving to teams

with a winning record. This suggests that it is easier for players moving together to import their existing

relationships in to low-performing teams than in to high-performing teams.

Together, the results highlight the double-edged sword of trading to improve the short-term

performance of a team. Trades may improve short-term performance by bringing in better players

and/or players that will eventually fit the team better. However, bringing in new players is disruptive to

all players on the team which erodes the short-term benefits of the trade.

For more information, see “Resetting the Shot Clock: The effect of comobility on human capital,” by

Benjamin Campbell, Brian Saxton, and Preeta Banerjee, which appeared in the February 2014 issue of